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noise_reduction

Remove background noise from audio recordings by applying noise reduction processing. First capture a noise profile from a silent section, then clean the desired audio selection.

Instructions

Apply noise reduction to the selected audio. You MUST call get_noise_profile first on a region of pure noise, then select the audio you want to clean, then call this.

WARNING: Values above 20 dB risk audible artifacts (warbling, metallic sound). Use 6-12 dB for gentle cleanup, 12-20 dB for moderate noise. Only exceed 20 dB for extremely noisy recordings where some artifact trade-off is acceptable.

Args: noise_reduction_db: Amount of noise reduction in dB (0-48). Default: 12 sensitivity: How sensitive the detection is (0-24). Default: 6 frequency_smoothing: Number of frequency smoothing bands (0-12). Default: 3

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noise_reduction_dbNo
sensitivityNo
frequency_smoothingNo
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses critical audio quality risks ('warbling, metallic sound') above 20 dB and the trade-off nature of aggressive settings. It could explicitly state whether the operation is destructive (modifies in-place) versus creating new audio, though the artifact warning implies modification.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Efficiently structured with prerequisite workflow, risk warning with specific thresholds, and parameter documentation. Every sentence adds value beyond the schema. The warning is appropriately emphasized with 'WARNING:' prefix.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero schema coverage, no annotations, and 3 parameters, the description is remarkably complete. It covers the prerequisite tool dependency, artifact risks with specific numeric thresholds, and full parameter documentation. No output schema exists, but the description appropriately focuses on the input requirements and side effects.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, requiring the description to compensate. The Args section documents all 3 parameters with ranges (0-48, 0-24, 0-12), units (dB, bands), defaults (12, 6, 3), and semantic meaning ('How sensitive the detection is', 'Number of frequency smoothing bands'). Fully compensates for schema deficiencies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with the specific action 'Apply noise reduction to the selected audio' (verb + resource + target). It clearly distinguishes this tool from its sibling get_noise_profile by stating the prerequisite workflow, and differentiates from other effect tools by specifying this is for noise cleanup specifically.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit sequencing requirements ('You MUST call get_noise_profile first... then select the audio... then call this'). Includes detailed parameter guidance mapping dB ranges to use cases (6-12 dB for gentle, 12-20 for moderate, >20 only for extreme cases), enabling correct invocation decisions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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